Consultant - Backend & Agentic AI Engineering

Job Summary

We're Hiring: Consultant - Backend Agentic AI Engineering at Chryselys

Location: Hyderabad

Job Type: Full-time

Role Overview

We are seeking an experienced Backend Agentic AI Engineer with 7-9 years of backend engineering experience across Python and/or Node.js, including at least 2.5 years of hands-on experience building production-grade GenAI, RAG, semantic layer, and agentic AI systems. The ideal candidate can design, build, scale, observe, evaluate, and continuously improve AI-enabled platforms that operate reliably in enterprise environments.

Experience Requirements

• 7-9 years of professional backend engineering experience, preferably building scalable APIs, services, data-intensive platforms, and distributed systems.
• Strong hands-on development experience in Python and/or Node.js.
• Minimum 2.5 years of practical experience in Agentic AI, Generative AI, RAG systems, semantic search, semantic layer design, or LLM-powered enterprise applications.
• Proven experience taking AI-enabled systems from concept to production, including reliability, security, monitoring, and performance considerations.

Responsibilities

• Design, build, and scale backend systems that support GenAI, RAG, semantic layer, and agentic AI use cases.
• Develop robust services and APIs using Python and/or Node.js with strong attention to performance, maintainability, and production readiness.
• Build semantic layers that enable structured access to enterprise knowledge, business entities, metadata, and domain concepts.
• Implement RAG pipelines including document ingestion, chunking, embeddings, indexing, semantic search, hybrid retrieval, ranking, grounding, and response generation.
• Engineer agentic workflows involving planning, tool usage, orchestration, memory, task routing, and multi-step reasoning patterns.
• Define and implement guardrails for safety, security, prompt injection protection, hallucination reduction, policy compliance, and controlled tool execution.
• Build observability modules for AI systems, including tracing, logging, metrics, prompt/response monitoring, retrieval quality tracking, latency, cost, and failure analysis.
• Evaluate and improve system performance using offline and online evaluation methods, golden datasets, retrieval metrics, LLM quality metrics, feedback loops, and experimentation.
• Collaborate with product, data science, platform, and business stakeholders to translate complex requirements into scalable technical solutions.

Technical Skills

• Languages: Python, Node.js, TypeScript/JavaScript.
• Backend Engineering: REST APIs, microservices, asynchronous processing, distributed systems, caching, queues, service reliability, and scalable system design.
• GenAI Agentic AI: LLM integration, prompt engineering, tool calling, agent orchestration, multi-agent workflows, planning patterns, and AI workflow frameworks.
• RAG Semantic Systems: embeddings, vector databases, semantic search, hybrid search, metadata filtering, re-ranking, knowledge retrieval, grounding, and semantic layer design.
• Cloud Deployment: containerized services, CI/CD, cloud-native deployment patterns, secure API integration, and production monitoring.
• Observability Evaluation: AI tracing, telemetry, evaluation datasets, retrieval precision/recall, hallucination tracking, response quality scoring, latency/cost optimization, and continuous improvement loops.

Must-Have Capabilities

• Strong backend engineering foundation with proven ability to build scalable, secure, and maintainable systems.
• Hands-on implementation experience with semantic layers, RAG pipelines, and enterprise knowledge retrieval systems.
• Practical exposure to agentic engineering, including workflow orchestration, tool integration, and controlled autonomous execution.
• Ability to define guardrails and governance mechanisms for safe and compliant AI behavior.
• Ability to instrument AI systems with observability, diagnostics, monitoring, and feedback capture.
• Ability to evaluate AI system quality and improve retrieval accuracy, response relevance, latency, reliability, and cost efficiency.

Good-to-Have Skills

• Experience with LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or similar orchestration frameworks.
• Experience with vector databases and search platforms such as Pinecone, Weaviate, Milvus, OpenSearch, Elasticsearch, pgvector, or similar technologies.
• Experience with cloud AI services, secure enterprise integrations, identity and access controls, and data privacy requirements.
• Exposure to healthcare, life sciences, pharma data, commercial analytics, or regulated enterprise environments.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.